In recent years, research has been made about object detection to detect various types of objects (such as a person's face and a car) from an image that is captured by a camera or the like. The object detection technology includes learning the features of objects to be detected to create learning data in advance, and comparing the created learning data and image data to determine whether the objects to be detected are included in the image.
The image data itself contains an enormous amount of information while the object detection technology has only to determine whether there is an object to be searched for in the image. The image data therefore needs to be reduced to save memory resources by utilizing information quantization techniques.
In some information quantization techniques, the image data is subjected to frequency conversion (wavelet transform) and quantization processing is performed on the basis of the magnitudes of the resulting conversion coefficients (or the magnitudes of differences in pixel value between adjoining pixels) (for example, see H. Schneiderman and T. Kanade, “Object Detection Using the Statistics of Parts”, International Journal of Computer Vision, 2002, which is referred to as “Schneiderman” hereinafter). According to such quantization processing, the image data is quantized in three levels, which allows a reduction of the area for storing the image data and learning data intended for object detection.
A technique has been known in which gradation difference values are calculated between different combinations of two pixels in target images that are made of grayscale images. The gradation difference values calculated are quantized in predetermined quantization levels for image collation (for example, see Japanese Laid-open Patent Publication No. 2004-246618).
In the foregoing conventional techniques, the image data is quantized to reduce the amount of data of the image data to be processed. With as many as three quantization levels, there has been a problem that the reduction in the amount of data is always optimally achieved.
When performing the quantization processing based on the magnitudes of the conversion coefficients resulting from the frequency conversion of the image data, it is possible to simply quantize the image data in two levels with reference to the median of the conversion coefficients. Such an approach is unrealistic, however, since facial images contain a lot of intermediate values. The quantized image data is less likely to preserve facial features, causing a significant drop in the accuracy of the object detection.